index int64 0 20.3k | text stringlengths 0 1.3M | year stringdate 1987-01-01 00:00:00 2024-01-01 00:00:00 | No stringlengths 1 4 |
|---|---|---|---|
6,400 | Unified Methods for Exploiting Piecewise Linear Structure in Convex Optimization Tyler B. Johnson University of Washington, Seattle tbjohns@washington.edu Carlos Guestrin University of Washington, Seattle guestrin@cs.washington.edu Abstract We develop methods for rapidly identifying important component... | 2016 | 466 |
6,401 | Large-Scale Price Optimization via Network Flow Shinji Ito NEC Corporation s-ito@me.jp.nec.com Ryohei Fujimaki NEC Corporation rfujimaki@nec-labs.com Abstract This paper deals with price optimization, which is to find the best pricing strategy that maximizes revenue or profit, on the basis of demand for... | 2016 | 467 |
6,402 | Generative Adversarial Imitation Learning Jonathan Ho OpenAI hoj@openai.com Stefano Ermon Stanford University ermon@cs.stanford.edu Abstract Consider learning a policy from example expert behavior, without interaction with the expert or access to a reinforcement signal. One approach is to recover the ... | 2016 | 468 |
6,403 | Truncated Variance Reduction: A Unified Approach to Bayesian Optimization and Level-Set Estimation Ilija Bogunovic1, Jonathan Scarlett1, Andreas Krause2, Volkan Cevher1 1 Laboratory for Information and Inference Systems (LIONS), EPFL 2 Learning and Adaptive Systems Group, ETH Z¨urich {ilija.bogunovic,jonathan.... | 2016 | 469 |
6,404 | Improving PAC Exploration Using the Median of Means Jason Pazis Laboratory for Information and Decision Systems Massachusetts Institute of Technology Cambridge, MA 02139, USA jpazis@mit.edu Ronald Parr Department of Computer Science Duke University Durham, NC 27708 parr@cs.duke.edu Jonathan P. H... | 2016 | 47 |
6,405 | f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization Sebastian Nowozin, Botond Cseke, Ryota Tomioka Machine Intelligence and Perception Group Microsoft Research {Sebastian.Nowozin, Botond.Cseke, ryoto}@microsoft.com Abstract Generative neural samplers are probabilistic mode... | 2016 | 470 |
6,406 | Nearly Isometric Embedding by Relaxation James McQueen Department of Statistics University of Washington Seattle, WA 98195 jmcq@u.washington.edu Marina Meil˘a Department of Statistics University of Washington Seattle, WA 98195 mmp@stat.washington.edu Dominique Perrault-Joncas Google Seattle, W... | 2016 | 471 |
6,407 | DECOrrelated feature space partitioning for distributed sparse regression Xiangyu Wang Dept. of Statistical Science Duke University wwrechard@gmail.com David Dunson Dept. of Statistical Science Duke University dunson@stat.duke.edu Chenlei Leng Dept. of Statistics University of Warwick C.Leng@w... | 2016 | 472 |
6,408 | Incremental Boosting Convolutional Neural Network for Facial Action Unit Recognition Shizhong Han, Zibo Meng, Ahmed Shehab Khan, Yan Tong Department of Computer Science & Engineering, University of South Carolina, Columbia, SC {han38, mengz, akhan}@email.sc.edu, tongy@cse.sc.edu Abstract Recognizing facial ... | 2016 | 473 |
6,409 | An urn model for majority voting in classification ensembles Victor Soto Computer Science Department Columbia University New York, NY, USA vsoto@cs.columbia.edu Alberto Suárez and Gonzalo Martínez-Muñoz Computer Science Department Universidad Autónoma de Madrid Madrid, Spain {gonzalo.martinez,alber... | 2016 | 474 |
6,410 | Unsupervised Feature Extraction by Time-Contrastive Learning and Nonlinear ICA Aapo Hyvärinen1,2 and Hiroshi Morioka1 1 Department of Computer Science and HIIT University of Helsinki, Finland 2 Gatsby Computational Neuroscience Unit University College London, UK Abstract Nonlinear independent component ... | 2016 | 475 |
6,411 | Leveraging Sparsity for Efficient Submodular Data Summarization Erik M. Lindgren, Shanshan Wu, Alexandros G. Dimakis The University of Texas at Austin Department of Electrical and Computer Engineering erikml@utexas.edu, shanshan@utexas.edu, dimakis@austin.utexas.edu Abstract The facility location problem i... | 2016 | 476 |
6,412 | Mistake Bounds for Binary Matrix Completion Mark Herbster University College London Department of Computer Science London WC1E 6BT, UK m.herbster@cs.ucl.ac.uk Stephen Pasteris University College London Department of Computer Science London WC1E 6BT, UK s.pasteris@cs.ucl.ac.uk Massimiliano Pontil ... | 2016 | 477 |
6,413 | Quantum Perceptron Models Nathan Wiebe Microsoft Research Redmond WA, 98052 nawiebe@microsoft.com Ashish Kapoor Microsoft Research Redmond WA, 98052 akapoor@microsoft.com Krysta M Svore Microsoft Research Redmond WA, 98052 ksvore@microsoft.com Abstract We demonstrate how quantum computation ... | 2016 | 478 |
6,414 | Direct Feedback Alignment Provides Learning in Deep Neural Networks Arild Nøkland Trondheim, Norway arild.nokland@gmail.com Abstract Artificial neural networks are most commonly trained with the back-propagation algorithm, where the gradient for learning is provided by back-propagating the error, layer b... | 2016 | 479 |
6,415 | Active Nearest-Neighbor Learning in Metric Spaces Aryeh Kontorovich Department of Computer Science Ben-Gurion University of the Negev Beer Sheva 8499000, Israel Sivan Sabato Department of Computer Science Ben-Gurion University of the Negev Beer Sheva 8499000, Israel Ruth Urner Max Planck Institute f... | 2016 | 48 |
6,416 | Average-case hardness of RIP certification Tengyao Wang Centre for Mathematical Sciences Cambridge, CB3 0WB, United Kingdom t.wang@statslab.cam.ac.uk Quentin Berthet Centre for Mathematical Sciences Cambridge, CB3 0WB, United Kingdom q.berthet@statslab.cam.ac.uk Yaniv Plan 1986 Mathematics Road Van... | 2016 | 480 |
6,417 | Efficient High-Order Interaction-Aware Feature Selection Based on Conditional Mutual Information Alexander Shishkin, Anastasia Bezzubtseva, Alexey Drutsa, Ilia Shishkov, Ekaterina Gladkikh, Gleb Gusev, Pavel Serdyukov Yandex; 16 Leo Tolstoy St., Moscow 119021, Russia {sisoid,nstbezz,adrutsa,ishfb,kglad,gleb57,... | 2016 | 481 |
6,418 | Fast and Provably Good Seedings for k-Means Olivier Bachem Department of Computer Science ETH Zurich olivier.bachem@inf.ethz.ch Mario Lucic Department of Computer Science ETH Zurich lucic@inf.ethz.ch S. Hamed Hassani Department of Computer Science ETH Zurich hamed@inf.ethz.ch Andreas Krause ... | 2016 | 482 |
6,419 | Anchor-Free Correlated Topic Modeling: Identifiability and Algorithm Kejun Huang∗ Xiao Fu∗ Nicholas D. Sidiropoulos Department of Electrical and Computer Engineering University of Minnesota Minneapolis, MN 55455, USA huang663@umn.edu xfu@umn.edu nikos@ece.umn.edu Abstract In topic modeling, many ... | 2016 | 483 |
6,420 | High Dimensional Structured Superposition Models Qilong Gu Dept of Computer Science & Engineering University of Minnesota, Twin Cities guxxx396@cs.umn.edu Arindam Banerjee Dept of Computer Science & Engineering University of Minnesota, Twin Cities banerjee@cs.umn.edu Abstract High dimensional superp... | 2016 | 484 |
6,421 | A Bandit Framework for Strategic Regression Yang Liu and Yiling Chen School of Engineering and Applied Science, Harvard University {yangl,yiling}@seas.harvard.edu Abstract We consider a learner’s problem of acquiring data dynamically for training a regression model, where the training data are collected from ... | 2016 | 485 |
6,422 | Linear Relaxations for Finding Diverse Elements in Metric Spaces Aditya Bhaskara University of Utah bhaskara@cs.utah.edu Mehrdad Ghadiri Sharif University of Technology ghadiri@ce.sharif.edu Vahab Mirrokni Google Research mirrokni@google.com Ola Svensson EPFL ola.svensson@epfl.ch Abstract ... | 2016 | 486 |
6,423 | Binarized Neural Networks Itay Hubara1* itayh@technion.ac.il Matthieu Courbariaux2* matthieu.courbariaux@gmail.com Daniel Soudry3 daniel.soudry@gmail.com Ran El-Yaniv1 rani@cs.technion.ac.il Yoshua Bengio2,4 yoshua.umontreal@gmail.com (1) Technion, Israel Institute of Technology. (2) Université ... | 2016 | 487 |
6,424 | A Appendix: Proof of Theorem 1 We first show that the estimate is unbiased. Indeed, for every i 6= j we can rewrite L(z) as E⇡`⇡(i),⇡(j)(z). Therefore, L(z) = 1 k2 −k X i6=j2[k] L(z) = 1 k2 −k X i6=j2[k] E ⇡`⇡(i),⇡(j)(z) = E ⇡L⇡(z) , which proves that the multibatch estimate is unbiased... | 2016 | 488 |
6,425 | Operator Variational Inference Rajesh Ranganath Princeton University Jaan Altosaar Princeton University Dustin Tran Columbia University David M. Blei Columbia University Abstract Variational inference is an umbrella term for algorithms which cast Bayesian inference as optimization. Classically, vari... | 2016 | 489 |
6,426 | Learning from Small Sample Sets by Combining Unsupervised Meta-Training with CNNs Yu-Xiong Wang Martial Hebert Robotics Institute, Carnegie Mellon University {yuxiongw, hebert}@cs.cmu.edu Abstract This work explores CNNs for the recognition of novel categories from few examples. Inspired by the transferab... | 2016 | 49 |
6,427 | Unsupervised Learning for Physical Interaction through Video Prediction Chelsea Finn∗ UC Berkeley cbfinn@eecs.berkeley.edu Ian Goodfellow OpenAI ian@openai.com Sergey Levine Google Brain UC Berkeley slevine@google.com Abstract A core challenge for an agent learning to interact with the world i... | 2016 | 490 |
6,428 | Full-Capacity Unitary Recurrent Neural Networks Scott Wisdom1∗, Thomas Powers1∗, John R. Hershey2, Jonathan Le Roux2, and Les Atlas1 1 Department of Electrical Engineering, University of Washington {swisdom, tcpowers, atlas}@uw.edu 2 Mitsubishi Electric Research Laboratories (MERL) {hershey, leroux}@merl.com ... | 2016 | 491 |
6,429 | Linear-Memory and Decomposition-Invariant Linearly Convergent Conditional Gradient Algorithm for Structured Polytopes Dan Garber Toyota Technological Institute at Chicago dgarber@ttic.edu Ofer Meshi Google meshi@google.com Abstract Recently, several works have shown that natural modifications of the ... | 2016 | 492 |
6,430 | Combining Adversarial Guarantees and Stochastic Fast Rates in Online Learning Wouter M. Koolen Centrum Wiskunde & Informatica Science Park 123, 1098 XG Amsterdam, the Netherlands wmkoolen@cwi.nl Peter Grünwald CWI and Leiden University pdg@cwi.nl Tim van Erven Leiden University Niels Bohrweg 1, ... | 2016 | 493 |
6,431 | A Comprehensive Linear Speedup Analysis for Asynchronous Stochastic Parallel Optimization from Zeroth-Order to First-Order Xiangru Lian*, Huan Zhang†, Cho-Jui Hsieh‡, Yijun Huang*, and Ji Liu* ∗Department of Computer Science, University of Rochester, USA † Department of Electrical and Computer Engineering, Un... | 2016 | 494 |
6,432 | Noise-Tolerant Life-Long Matrix Completion via Adaptive Sampling Maria-Florina Balcan Machine Learning Department Carnegie Mellon University, USA ninamf@cs.cmu.edu Hongyang Zhang Machine Learning Department Carnegie Mellon University, USA hongyanz@cs.cmu.edu Abstract We study the problem of recove... | 2016 | 495 |
6,433 | Satisfying Real-world Goals with Dataset Constraints Gabriel Goh Dept. of Mathematics UC Davis Davis, CA 95616 ggoh@math.ucdavis.edu Andrew Cotter, Maya Gupta Google Inc. 1600 Amphitheatre Parkway Mountain View, CA 94043 acotter@google.com mayagupta@google.com Michael Friedlander Dept. of Comp... | 2016 | 496 |
6,434 | Launch and Iterate: Reducing Prediction Churn Q. Cormier ENS Lyon 15 parvis René Descartes Lyon, France quentin.cormier@ens-lyon.fr M. Milani Fard, K. Canini, M. R. Gupta Google Inc. 1600 Amphitheatre Parkway Mountain View, CA 94043 {mmilanifard,canini,mayagupta}@google.com Abstract Practical ap... | 2016 | 497 |
6,435 | Constraints Based Convex Belief Propagation Yaniv Tenzer Department of Statistics The Hebrew University Alexander Schwing Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign Kevin Gimpel Toyota Technological Institute at Chicago Tamir Hazan Faculty of Industr... | 2016 | 498 |
6,436 | Data driven estimation of Laplace-Beltrami operator Frédéric Chazal Inria Saclay Palaiseau France frederic.chazal@inria.fr Ilaria Giulini Inria Saclay Palaiseau France ilaria.giulini@me.com Bertrand Michel Ecole Centrale de Nantes Laboratoire de Mathématiques Jean Leray (UMR 6629 CNRS) Nantes Fr... | 2016 | 499 |
6,437 | Conditional Generative Moment-Matching Networks Yong Ren, Jialian Li, Yucen Luo, Jun Zhu∗ Dept. of Comp. Sci. & Tech., TNList Lab; Center for Bio-Inspired Computing Research State Key Lab for Intell. Tech. & Systems, Tsinghua University, Beijing, China {renyong15, luoyc15, jl12}@mails.tsinghua.edu.cn; dcszj@tsi... | 2016 | 5 |
6,438 | Learning Bayesian networks with ancestral constraints Eunice Yuh-Jie Chen and Yujia Shen and Arthur Choi and Adnan Darwiche Computer Science Department University of California Los Angeles, CA 90095 {eyjchen,yujias,aychoi,darwiche}@cs.ucla.edu Abstract We consider the problem of learning Bayesian networ... | 2016 | 50 |
6,439 | The Robustness of Estimator Composition Pingfan Tang School of Computing University of Utah Salt Lake City, UT 84112 tang1984@cs.utah.edu Jeff M. Phillips School of Computing University of Utah Salt Lake City, UT 84112 jeffp@cs.utah.edu Abstract We formalize notions of robustness for composite e... | 2016 | 500 |
6,440 | Active Learning from Imperfect Labelers Songbai Yan University of California, San Diego yansongbai@eng.ucsd.edu Kamalika Chaudhuri University of California, San Diego kamalika@cs.ucsd.edu Tara Javidi University of California, San Diego tjavidi@eng.ucsd.edu Abstract We study active learning where t... | 2016 | 501 |
6,441 | Improved Variational Inference with Inverse Autoregressive Flow Diederik P. Kingma dpkingma@openai.com Tim Salimans tim@openai.com Rafal Jozefowicz rafal@openai.com Xi Chen peter@openai.com Ilya Sutskever ilya@openai.com Max Welling⇤ M.Welling@uva.nl Abstract The framework of normalizing fl... | 2016 | 502 |
6,442 | Select-and-Sample for Spike-and-Slab Sparse Coding Abdul-Saboor Sheikh Technical University of Berlin, Germany, and Cluster of Excellence Hearing4all University of Oldenburg, Germany, and SAP Innovation Center Network, Berlin sheikh.abdulsaboor@gmail.com Jörg Lücke Research Center Neurosensory Science ... | 2016 | 503 |
6,443 | Learning Infinite RBMs with Frank-Wolfe Wei Ping∗ Qiang Liu† Alexander Ihler∗ ∗Computer Science, UC Irvine †Computer Science, Dartmouth College {wping,ihler}@ics.uci.edu qliu@cs.dartmouth.edu Abstract In this work, we propose an infinite restricted Boltzmann machine (RBM), whose maximum likelihood est... | 2016 | 504 |
6,444 | Faster Projection-free Convex Optimization over the Spectrahedron Dan Garber Toyota Technological Institute at Chicago dgarber@ttic.edu Abstract Minimizing a convex function over the spectrahedron, i.e., the set of all d ⇥d positive semidefinite matrices with unit trace, is an important optimization task ... | 2016 | 505 |
6,445 | Improved Regret Bounds for Oracle-Based Adversarial Contextual Bandits Vasilis Syrgkanis Microsoft Research vasy@microsoft.com Haipeng Luo Microsoft Research haipeng@microsoft.com Akshay Krishnamurthy University of Massachusetts, Amherst akshay@cs.umass.edu Robert E. Schapire Microsoft Research ... | 2016 | 506 |
6,446 | Joint quantile regression in vector-valued RKHSs Maxime Sangnier Olivier Fercoq Florence d’Alch´e-Buc LTCI, CNRS, T´el´ecom ParisTech Universit´e Paris-Saclay 75013, Paris, France {maxime.sangnier, olivier.fercoq, florence.dalche} @telecom-paristech.fr Abstract Addressing the will to give a more com... | 2016 | 507 |
6,447 | Kernel Bayesian Inference with Posterior Regularization Yang Song†, Jun Zhu‡∗, Yong Ren‡ † Dept. of Physics, Tsinghua University, Beijing, China ‡ Dept. of Comp. Sci. & Tech., TNList Lab; Center for Bio-Inspired Computing Research State Key Lab for Intell. Tech. & Systems, Tsinghua University, Beijing, China ... | 2016 | 508 |
6,448 | Scaled Least Squares Estimator for GLMs in Large-Scale Problems Murat A. Erdogdu Department of Statistics Stanford University erdogdu@stanford.edu Mohsen Bayati Graduate School of Business Stanford University bayati@stanford.edu Lee H. Dicker Department of Statistics and Biostatistics Rutgers Un... | 2016 | 509 |
6,449 | Exponential expressivity in deep neural networks through transient chaos Ben Poole1, Subhaneil Lahiri1, Maithra Raghu2, Jascha Sohl-Dickstein2, Surya Ganguli1 1Stanford University, 2Google Brain {benpoole,sulahiri,sganguli}@stanford.edu, {maithra,jaschasd}@google.com Abstract We combine Riemannian geometry ... | 2016 | 51 |
6,450 | Contextual semibandits via supervised learning oracles Akshay Krishnamurthy† Alekh Agarwal‡ Miroslav Dudík‡ akshay@cs.umass.edu alekha@microsoft.com mdudik@microsoft.com †College of Information and Computer Sciences ‡Microsoft Research University of Massachusetts, Amherst, MA New York, NY Abstract... | 2016 | 510 |
6,451 | Learning Treewidth-Bounded Bayesian Networks with Thousands of Variables Mauro Scanagatta IDSIA∗, SUPSI†, USI‡ Lugano, Switzerland mauro@idsia.ch Giorgio Corani IDSIA∗, SUPSI†, USI‡ Lugano, Switzerland giorgio@idsia.ch Cassio P. de Campos Queen’s University Belfast Northern Ireland, UK c.decam... | 2016 | 511 |
6,452 | Unsupervised Learning from Noisy Networks with Applications to Hi-C Data Bo Wang⇤1, Junjie Zhu2, Oana Ursu3, Armin Pourshafeie4, Serafim Batzoglou1 and Anshul Kundaje3,1 1Department of Computer Science, Stanford University 2Department of Electrical Engineering, Stanford University 3Department of Genetics, Stan... | 2016 | 512 |
6,453 | Scan Order in Gibbs Sampling: Models in Which it Matters and Bounds on How Much Bryan He, Christopher De Sa, Ioannis Mitliagkas, and Christopher Ré Stanford University {bryanhe,cdesa,imit,chrismre}@stanford.edu Abstract Gibbs sampling is a Markov Chain Monte Carlo sampling technique that iteratively sampl... | 2016 | 513 |
6,454 | Deep Neural Networks with Inexact Matching for Person Re-Identification Arulkumar Subramaniam Indian Institute of Technology Madras Chennai, India 600036 aruls@cse.iitm.ac.in Moitreya Chatterjee Indian Institute of Technology Madras Chennai, India 600036 metro.smiles@gmail.com Anurag Mittal Indian ... | 2016 | 514 |
6,455 | Efficient Neural Codes under Metabolic Constraints Zhuo Wang ∗† Department of Mathematics University of Pennsylvania wangzhuo@nyu.edu Xue-Xin Wei ∗‡ Department of Psychology University of Pennsylvania weixxpku@gmail.com Alan A. Stocker Department of Psychology University of Pennsylvania astocker@... | 2016 | 515 |
6,456 | Learning Kernels with Random Features Aman Sinha1 John Duchi1,2 Departments of 1Electrical Engineering and 2Statistics Stanford University {amans,jduchi}@stanford.edu Abstract Randomized features provide a computationally efficient way to approximate kernel machines in machine learning tasks. However, su... | 2016 | 516 |
6,457 | Combinatorial semi-bandit with known covariance Rémy Degenne LMPA, Université Paris Diderot CMLA, ENS Paris-Saclay degenne@cmla.ens-cachan.fr Vianney Perchet CMLA, ENS Paris-Saclay CRITEO Research, Paris perchet@normalesup.org Abstract The combinatorial stochastic semi-bandit problem is an extension... | 2016 | 517 |
6,458 | Perspective Transformer Nets: Learning Single-View 3D Object Reconstruction without 3D Supervision Xinchen Yan1 Jimei Yang2 Ersin Yumer2 Yijie Guo1 Honglak Lee1,3 1University of Michigan, Ann Arbor 2Adobe Research 3Google Brain {xcyan,guoyijie,honglak}@umich.edu, {jimyang,yumer}@adobe.com Abstract... | 2016 | 518 |
6,459 | Exact Recovery of Hard Thresholding Pursuit Xiao-Tong Yuan B-DAT Lab Nanjing University of Info. Sci.&Tech. Nanjing, Jiangsu, 210044, China xtyuan@nuist.edu.cn Ping Li†‡ Tong Zhang† †Depart. of Statistics and ‡Depart. of CS Rutgers University Piscataway, NJ, 08854, USA {pingli,tzhang}@stat.rutgers... | 2016 | 519 |
6,460 | MetaGrad: Multiple Learning Rates in Online Learning Tim van Erven Leiden University tim@timvanerven.nl Wouter M. Koolen Centrum Wiskunde & Informatica wmkoolen@cwi.nl Abstract In online convex optimization it is well known that certain subclasses of objective functions are much easier than arbitrar... | 2016 | 52 |
6,461 | Parameter Learning for Log-supermodular Distributions Tatiana Shpakova INRIA - École Normale Supérieure Paris tatiana.shpakova@inria.fr Francis Bach INRIA - École Normale Supérieure Paris francis.bach@inria.fr Abstract We consider log-supermodular models on binary variables, which are probabilistic ... | 2016 | 520 |
6,462 | A Multi-step Inertial Forward–Backward Splitting Method for Non-convex Optimization Jingwei Liang and Jalal M. Fadili Normandie Univ, ENSICAEN, CNRS, GREYC {Jingwei.Liang,Jalal.Fadili}@greyc.ensicaen.fr Gabriel Peyré CNRS, DMA, ENS Paris Gabriel.Peyre@ens.fr Abstract We propose a multi-step inertial F... | 2016 | 521 |
6,463 | Optimal Binary Classifier Aggregation for General Losses Akshay Balsubramani University of California, San Diego abalsubr@ucsd.edu Yoav Freund University of California, San Diego yfreund@ucsd.edu Abstract We address the problem of aggregating an ensemble of predictors with known loss bounds in a semi... | 2016 | 522 |
6,464 | Dense Associative Memory for Pattern Recognition Dmitry Krotov Simons Center for Systems Biology Institute for Advanced Study Princeton, USA krotov@ias.edu John J. Hopfield Princeton Neuroscience Institute Princeton University Princeton, USA hopfield@princeton.edu Abstract A model of associative ... | 2016 | 523 |
6,465 | Fairness in Learning: Classic and Contextual Bandits ∗ Matthew Joseph Michael Kearns Jamie Morgenstern Aaron Roth University of Pennsylvania, Department of Computer and Information Science majos, mkearns, jamiemor, aaroth@cis.upenn.edu Abstract We introduce the study of fairness in multi-armed bandit pr... | 2016 | 524 |
6,466 | Variational Autoencoder for Deep Learning of Images, Labels and Captions Yunchen Pu†, Zhe Gan†, Ricardo Henao†, Xin Yuan‡, Chunyuan Li†, Andrew Stevens† and Lawrence Carin† †Department of Electrical and Computer Engineering, Duke University {yp42, zg27, r.henao, cl319, ajs104, lcarin}@duke.edu ‡Nokia Bell L... | 2016 | 525 |
6,467 | Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks Tim Salimans OpenAI tim@openai.com Diederik P. Kingma OpenAI dpkingma@openai.com Abstract We present weight normalization: a reparameterization of the weight vectors in a neural network that decouples th... | 2016 | 526 |
6,468 | Learning Additive Exponential Family Graphical Models via ℓ2,1-norm Regularized M-Estimation Xiao-Tong Yuan† Ping Li‡§ Tong Zhang‡ Qingshan Liu† Guangcan Liu† †B-DAT Lab, Nanjing University of Info. Sci.&Tech. Nanjing, Jiangsu, 210044, China ‡Depart. of Statistics and §Depart. of Computer Science, Rutge... | 2016 | 527 |
6,469 | Disentangling factors of variation in deep representations using adversarial training Michael Mathieu, Junbo Zhao, Pablo Sprechmann, Aditya Ramesh, Yann LeCun 719 Broadway, 12th Floor, New York, NY 10003 {mathieu, junbo.zhao, pablo, ar2922, yann}@cs.nyu.edu Abstract We introduce a conditional generative mod... | 2016 | 528 |
6,470 | Gaussian Processes for Survival Analysis Tamara Fernández Department of Statistics, University of Oxford. Oxford, UK. fernandez@stats.ox.ac.uk Nicolás Rivera Department of Informatics, King’s College London. London, UK. nicolas.rivera@kcl.ac.uk Yee Whye Teh Department of Statistics, University... | 2016 | 529 |
6,471 | Learning under uncertainty: a comparison between R-W and Bayesian approach He Huang Laureate Institute for Brain Research Tulsa, OK, 74133 crane081@gmail.com Martin Paulus Laureate Institute for Brain Research Tulsa, OK, 74133 mpaulus@laureateinstitute.org Abstract Accurately differentiating betwe... | 2016 | 53 |
6,472 | Correlated-PCA: Principal Components’ Analysis when Data and Noise are Correlated Namrata Vaswani and Han Guo Iowa State University, Ames, IA, USA Email: {namrata,hanguo}@iastate.edu Abstract Given a matrix of observed data, Principal Components Analysis (PCA) computes a small number of orthogonal directi... | 2016 | 530 |
6,473 | On Explore-Then-Commit Strategies Aurélien Garivier∗ Institut de Mathématiques de Toulouse; UMR5219 Université de Toulouse; CNRS UPS IMT, F-31062 Toulouse Cedex 9, France aurelien.garivier@math.univ-toulouse.fr Emilie Kaufmann Univ. Lille, CNRS, Centrale Lille, Inria SequeL UMR 9189, CRIStAL - Centre de... | 2016 | 531 |
6,474 | Adaptive Neural Compilation Rudy Bunel∗ Alban Desmaison∗ University of Oxford University of Oxford rudy@robots.ox.ac.uk alban@robots.ox.ac.uk Pushmeet Kohli Philip H.S. Torr M. Pawan Kumar Microsoft Research University of Oxford University of Oxford pkohli@microsoft.com philip.torr@eng.ox.ac... | 2016 | 532 |
6,475 | Graphical Time Warping for Joint Alignment of Multiple Curves Yizhi Wang Virginia Tech yzwang@vt.edu David J. Miller Pennsylvania State University djmiller@engr.psu.edu Kira Poskanzer University of California, San Francisco Kira.Poskanzer@ucsf.edu Yue Wang Virginia Tech yuewang@vt.edu Lin Ti... | 2016 | 533 |
6,476 | PerforatedCNNs: Acceleration through Elimination of Redundant Convolutions Michael Figurnov1,2, Aijan Ibraimova4, Dmitry Vetrov1,3, and Pushmeet Kohli5 1National Research University Higher School of Economics 2Lomonosov Moscow State University 3Yandex 4Skolkovo Institute of Science and Technology 5Microsoft Res... | 2016 | 534 |
6,477 | DeepMath - Deep Sequence Models for Premise Selection Alexander A. Alemi ∗ Google Inc. alemi@google.com François Chollet ∗ Google Inc. fchollet@google.com Niklas Een ∗ Google Inc. een@google.com Geoffrey Irving ∗ Google Inc. geoffreyi@google.com Christian Szegedy ∗ Google Inc. szegedy@go... | 2016 | 535 |
6,478 | A Pseudo-Bayesian Algorithm for Robust PCA Tae-Hyun Oh1 Yasuyuki Matsushita2 In So Kweon1 David Wipf3∗ 1Electrical Engineering, KAIST, Daejeon, South Korea 2Multimedia Engineering, Osaka University, Osaka, Japan 3Microsoft Research, Beijing, China thoh.kaist.ac.kr@gmail.com yasumat@ist.osaka-u.ac.jp ... | 2016 | 536 |
6,479 | The Forget-me-not Process Kieran Milan†, Joel Veness†, James Kirkpatrick, Demis Hassabis Google DeepMind {kmilan,aixi,kirkpatrick,demishassabis}@google.com Anna Koop, Michael Bowling University of Alberta {anna,bowling}@cs.ualberta.ca Abstract We introduce the Forget-me-not Process, an efficient, non-par... | 2016 | 537 |
6,480 | Unsupervised Risk Estimation Using Only Conditional Independence Structure Jacob Steinhardt Stanford University jsteinhardt@cs.stanford.edu Percy Liang Stanford University pliang@cs.stanford.edu Abstract We show how to estimate a model’s test error from unlabeled data, on distributions very differen... | 2016 | 538 |
6,481 | Deep Learning without Poor Local Minima Kenji Kawaguchi Massachusetts Institute of Technology kawaguch@mit.edu Abstract In this paper, we prove a conjecture published in 1989 and also partially address an open problem announced at the Conference on Learning Theory (COLT) 2015. With no unrealistic assumpti... | 2016 | 539 |
6,482 | End-to-End Goal-Driven Web Navigation Rodrigo Nogueira Tandon School of Engineering New York University rodrigonogueira@nyu.edu Kyunghyun Cho Courant Institute of Mathematical Sciences New York University kyunghyun.cho@nyu.edu Abstract We propose a goal-driven web navigation as a benchmark task for ... | 2016 | 54 |
6,483 | Linear Contextual Bandits with Knapsacks Shipra Agrawal∗ Nikhil R. Devanur† Abstract We consider the linear contextual bandit problem with resource consumption, in addition to reward generation. In each round, the outcome of pulling an arm is a reward as well as a vector of resource consumptions. The expect... | 2016 | 540 |
6,484 | High resolution neural connectivity from incomplete tracing data using nonnegative spline regression Kameron Decker Harris Applied Mathematics, U. of Washington kamdh@uw.edu Stefan Mihalas Allen Institute for Brain Science Applied Mathematics, U. of Washington stefanm@alleninstitute.org Eric Shea-Brow... | 2016 | 541 |
6,485 | Learning and Forecasting Opinion Dynamics in Social Networks Abir De∗ Isabel Valera† Niloy Ganguly∗ Sourangshu Bhattacharya∗ Manuel Gomez-Rodriguez† IIT Kharagpur∗ MPI for Software Systems† {abir.de,niloy,sourangshu}@cse.iitkgp.ernet.in {ivalera,manuelgr}@mpi-sws.org Abstract Social media and so... | 2016 | 542 |
6,486 | Lifelong Learning with Weighted Majority Votes Anastasia Pentina IST Austria apentina@ist.ac.at Ruth Urner Max Planck Institute for Intelligent Systems rurner@tuebingen.mpg.de Abstract Better understanding of the potential benefits of information transfer and representation learning is an important step ... | 2016 | 543 |
6,487 | Depth from a Single Image by Harmonizing Overcomplete Local Network Predictions Ayan Chakrabarti TTI-Chicago Chicago, IL ayanc@ttic.edu Jingyu Shao Dept. of Statistics, UCLA∗ Los Angeles, CA shaojy15@ucla.edu Gregory Shakhnarovich TTI-Chicago Chicago, IL gregory@ttic.edu Abstract A single ... | 2016 | 544 |
6,488 | Ancestral Causal Inference Sara Magliacane VU Amsterdam & University of Amsterdam sara.magliacane@gmail.com Tom Claassen Radboud University Nijmegen tomc@cs.ru.nl Joris M. Mooij University of Amsterdam j.m.mooij@uva.nl Abstract Constraint-based causal discovery from limited data is a notoriously d... | 2016 | 545 |
6,489 | Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation Tejas D. Kulkarni∗ DeepMind, London tejasdkulkarni@gmail.com Karthik R. Narasimhan∗ CSAIL, MIT karthikn@mit.edu Ardavan Saeedi CSAIL, MIT ardavans@mit.edu Joshua B. Tenenbaum BCS, MIT jbt@mit.... | 2016 | 546 |
6,490 | Agnostic Estimation for Misspecified Phase Retrieval Models Matey Neykov Zhaoran Wang Han Liu Department of Operations Research and Financial Engineering Princeton University, Princeton, NJ 08544 {mneykov, zhaoran, hanliu}@princeton.edu Abstract The goal of noisy high-dimensional phase retrieval is to ... | 2016 | 547 |
6,491 | Can Peripheral Representations Improve Clutter Metrics on Complex Scenes? Arturo Deza Dynamical Neuroscience Institute for Collaborative Biotechnologies UC Santa Barbara, CA, USA deza@dyns.ucsb.edu Miguel P. Eckstein Psychological and Brain Sciences Institute for Collaborative Biotechnologies UC San... | 2016 | 548 |
6,492 | Proximal Deep Structured Models Shenlong Wang University of Toronto slwang@cs.toronto.edu Sanja Fidler University of Toronto fidler@cs.toronto.edu Raquel Urtasun University of Toronto urtasun@cs.toronto.edu Abstract Many problems in real-world applications involve predicting continuous-valued ra... | 2016 | 549 |
6,493 | Higher-Order Factorization Machines Mathieu Blondel, Akinori Fujino, Naonori Ueda NTT Communication Science Laboratories Japan Masakazu Ishihata Hokkaido University Japan Abstract Factorization machines (FMs) are a supervised learning approach that can use second-order feature combinations even when t... | 2016 | 55 |
6,494 | SPALS: Fast Alternating Least Squares via Implicit Leverage Scores Sampling Dehua Cheng University of Southern California dehua.cheng@usc.edu Richard Peng Georgia Institute of Technology rpeng@cc.gatech.edu Ioakeim Perros Georgia Institute of Technology perros@gatech.edu Yan Liu University of So... | 2016 | 550 |
6,495 | On Multiplicative Integration with Recurrent Neural Networks Yuhuai Wu1,∗, Saizheng Zhang2,∗, Ying Zhang2, Yoshua Bengio2,4 and Ruslan Salakhutdinov3,4 1University of Toronto, 2MILA, Université de Montréal, 3Carnegie Mellon University, 4CIFAR ywu@cs.toronto.edu,2{firstname.lastname}@umontreal.ca,rsalakhu@cs.cmu... | 2016 | 551 |
6,496 | The Generalized Reparameterization Gradient Francisco J. R. Ruiz University of Cambridge Columbia University Michalis K. Titsias Athens University of Economics and Business David M. Blei Columbia University Abstract The reparameterization gradient has become a widely used method to obtain Monte Ca... | 2016 | 552 |
6,497 | Semiparametric Differential Graph Models Pan Xu University of Virginia px3ds@virginia.edu Quanquan Gu University of Virginia qg5w@virginia.edu Abstract In many cases of network analysis, it is more attractive to study how a network varies under different conditions than an individual static network. W... | 2016 | 553 |
6,498 | Neural Universal Discrete Denoiser Taesup Moon DGIST Daegu, Korea 42988 tsmoon@dgist.ac.kr Seonwoo Min, Byunghan Lee, Sungroh Yoon Seoul National University Seoul, Korea 08826 {mswzeus, styxkr, sryoon}@snu.ac.kr Abstract We present a new framework of applying deep neural networks (DNN) to devise a ... | 2016 | 554 |
6,499 | Testing for Differences in Gaussian Graphical Models: Applications to Brain Connectivity Eugene Belilovsky 1,2,3, Gael Varoquaux2, Matthew Blaschko3 1University of Paris-Saclay, 2INRIA, 3KU Leuven {eugene.belilovsky, gael.varoquaux } @inria.fr matthew.blaschko@esat.kuleuven.be Abstract Functional brain ne... | 2016 | 555 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.